Method for dynamically and economically dispatching power system based on optimal load transfer ratio and optimal grid connection ratio of wind power and photovoltaic power

ABSTRACT

A method for dispatching a power system based on optimal load transfer ratio and optimal grid connection ratio of wind power and photovoltaic power includes: acquiring load data; drawing a load curve; defining a peak load period, a flat load period and a low load period, and calculating average loads of the peak load period, the flat load period and the low load period before a load transfer; determining value ranges of a peak-low load transfer ratio, a peak-flat load transfer ratio and a flat-low load transfer ratio; establishing an objective function considering generation cost of thermal power unit, wind power purchase cost, PV power purchase cost and compensation cost for consumer load transfer; introducing an immune algorithm to calculate grid connection ratio of wind power, grid connection ratio of PV power, peak-low load transfer ratio, peak-flat load transfer ratio and flat-low load ratio corresponding to a minimum operating cost.

CROSS REFERENCE TO THE RELATED APPLICATIONS

This application is based upon and claims priority to Chinese PatentApplication No. 201910734752.3, filed on Aug. 9, 2019, the entirecontents of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention belongs to the technical field of powerdistribution systems, and particularly relates to a method fordynamically and economically dispatching a power system based on anoptimal load transfer ratio and an optimal grid connection ratio of windpower and photovoltaic (PV) power.

BACKGROUND

China's renewable energy installed capacity continues to grow rapidly.At the end of 2018, the installed capacity of renewable energygeneration in China reached 729 million kilowatts, including 184 millionkilowatts of wind power and 175 million kilowatts of photovoltaic (PV)power. However, existing systems for optimal dispatching of powerfunction to optimize dispatching only on the front end where power isgenerated. Wind and solar energy are substantially curtailed as aresult. Grid companies are therefore actively exploring a“source-grid-load” coordinated operation mode to promote consumption ofrenewable energy. Demand response is an important logistic upon which tobase hub-type, platform-type and shared-type enterprise creation, strongsmart grid (SSG) development and a ubiquitous power Internet of Things(UPIoT). It is necessary to fully exploit demand-side resources andstrengthen the interaction between grid companies and power consumers tofacilitate consumer choice. Grid companies have taken steps toincentivize power consumers to transfer loads, that is, to reduce powerconsumption during peak load periods and increase power consumptionduring flat or low load periods. These companies have also integratedthe grid connection ratios of wind power and PV power into theirbusiness models to maximize economic benefits of system operation. Suchrealignment can substantially reduce the peak-to-low load difference,increase the renewable energy consumption and the flexibility of powersystem dispatching, and achieve a “win-win” for grid companies and powerconsumers alike.

Currently, however, the demand-side resources are not fully developed inthe power system operation. Instead, the allocation of resources isdisproportionately optimized by the dispatching of power sources on thegeneration side, i.e., the front end, which makes the problem of windand solar energy curtailment still very serious. Therefore, it is highlydesirable to provide a new reliable method for optimally dispatching apower system.

SUMMARY

An objective of the present invention is to provide a method fordynamically and economically dispatching a power system based on anoptimal load transfer ratio and an optimal grid connection ratio of windpower and photovoltaic (PV) power. The present invention simultaneouslytakes into account the resources on both generation and consumptionsides, further mitigating the curtailment of wind and solar energy andincreasing the renewable energy consumption and the flexibility of powersystem dispatching.

To achieve the above purpose, the present invention provides a methodfor dynamically and economically dispatching a power system based on anoptimal load transfer ratio and an optimal grid connection ratio of windpower and photovoltaic (PV) power, including the following steps:

S1: acquiring initial data by taking a day with 24 periods (each periodbeing 1 h) as a dispatching period, wherein the initial data includesoutput power P_(w) of a wind turbine in the system, output power P_(pv)of a PV power station in the system and load power P_(L) of the system;

S2: drawing a wind power output curve, a PV power output curve and aload curve according to the acquired initial data;

S3: defining a peak period T_(p), a flat period T_(f) and a low periodT_(l) according to the drawn load curve of the system, and calculatingan average load P_(p,ave) of the peak period, an average load P_(f,ave)of flat period, and an average load P_(l,ave) of the low period before aload transfer;

S4: determining a value range of a peak-low load transfer ratio μ_(pl),a value range of a peak-flat load transfer ratio μ_(pf) and a valuerange of a flat-low load transfer ratio μ_(fl) respectively according tothe P_(p,ave), P_(f,ave) and P_(l,ave) in S3;

S5: assuming that total load power before and after the load transfer isunchanged, then calculating a system load after the load transfer, andestablishing an objective function minC about a system operating costafter the load transfer, the system operating cost including: ageneration cost C_(G) of a thermal power unit, a wind power purchasecost C_(W), a PV power purchase cost C_(PV) and a compensation costC_(M) for consumer load transfer;

S6: entering constraints, including: system power balance constraint,positive and negative spinning reserve constraint, thermal power unitoutput constraint, thermal power unit ramp constraint, wind powergrid-connection ratio μ_(w) constraint, PV power grid-connection ratioμ_(pv) constraint, peak-low load transfer ratio μ_(pl) constraint,peak-flat load transfer ratio μ_(pf) constraint and flat-low loadtransfer ratio μ_(fl) constraint;

S7: introducing an immune algorithm (IA) to solve and generate aninitial population; performing immune operations on the population,including selection, cloning, mutation and cloning suppression;refreshing the population until a termination condition is met, andobtaining μ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl) corresponding to aminimum system operating cost.

Further, in S1, the output power P_(w) of the wind turbine in thesystem, the output power P_(pv) of the PV power station in the systemand the load power P_(L) of the system are expressed as follows:

P _(w) ={P _(w1) ,P _(w2) ,Λ,P _(wr) ,Λ,P _(w24)}

P _(pv) ={P _(pv1) ,P _(pv2) ,Λ,P _(pvt) ,Λ,P _(pv24)}

P _(L) {P _(L1) ,P _(L2) ,Λ,P _(Lt) ,Λ,P _(L24)}

where, P_(wt) represents output power of the wind turbine in a t-thperiod, P_(pvt) represents output power of the PV power station in thet-th period, and P_(Lt) represents load power in the t-th period, t=1,2, . . . , 24.

Further, the peak period T_(p), the flat period T_(f) and the low periodT_(l) in S3 are calculated as follows:

S31: calculating a total daily load Q_(L),

${Q_{L} = {\sum\limits_{t = 1}^{24}P_{Lt}}};$

S32: calculating an hourly average load Q_(L)/24;

S33: regarding the t-th period as the peak period T_(p) whenP_(Lt)>(1+k)·(Q_(L)/24) regarding the t-th period as the low periodT_(l) when P_(Lt)<(1−k)·(Q_(L)/24), and regarding the t-th period as theflat period T_(f) when (1−k)·(Q_(L)/24)≤P_(Lt)≤(1+k)·(Q_(L)/24), whereink is a dividing factor of a peak-low period, a flat-low period and a lowperiod, 0<k<1.

Further, S4 of determining the value range of the peak-low load transferratio μ_(pl), the value range of the peak-flat load transfer ratioμ_(pf) and the value range of the flat-low load transfer ratio μ_(fl)respectively according to the P_(p,ave), P_(f,ave) and P_(l,ave) in S3is as follows:

$\left\{ \begin{matrix}{\mu_{pl} \in \left\lbrack {0,\frac{P_{p,{ave}} - P_{l,{ave}}}{P_{p,{ave}}}} \right\rbrack} \\{\mu_{pf} \in \left\lbrack {0,\frac{P_{p,{ave}} - P_{f,{ave}}}{P_{p,{ave}}}} \right\rbrack} \\{\mu_{fl} \in \left\lbrack {0,\frac{P_{f,{ave}} - P_{l,{ave}}}{P_{f,{ave}}}} \right\rbrack}\end{matrix} \right..$

Further, in S5, the objective function minC is expressed as:

min C=C _(G) +C _(W) +C _(PV) +C _(M)

where, C_(W)=θ_(w)×Q_(w), θ_(w) and Q_(w) represent an on-grid windpower tariff and on-grid wind power, respectively;

${Q_{w} = {\mu_{w} \times {\sum\limits_{t = 1}^{24}P_{wt}}}},$

μ_(w) represents a grid connection ratio of wind power;

C_(PV)=θ_(pv)×Q_(pv), θ_(pv) and Q_(pv) represent an on-grid PV powertariff and on-grid PV power, respectively;

${Q_{pv} = {\mu_{pv} \times {\sum\limits_{t = 1}^{24}P_{pvt}}}},$

μ_(pv) represents a grid connection ratio of PV power;

$C_{M} = \left\{ {\begin{matrix}{{\theta_{m1} \times Q_{tran}},} & {Q_{tran} \leq {\lambda Q_{L}}} \\{{{\theta_{m1} \times \lambda Q_{L}} + {\theta_{m2} \times \left( {Q_{tran} - {\lambda Q_{L}}} \right)}},} & {Q_{tran} > {\lambda Q_{L}}}\end{matrix},} \right.$

λ is a dividing factor of a conventional load and a deep load, 0<λ<1;Q_(tran) is a transferred load,

${Q_{tran} = {\sum\limits_{t = 1}^{24}\left( {P_{Dt} - P_{Lt}} \right)}};$

θ_(m1) is a compensation cost for a conventional load transfer; θ_(m2)is a compensation cost for a deep load transfer; the load of the systemin the t-th period after the load transfer is expressed as:

${P_{Dt} = \left\{ \begin{matrix}{{P_{Lt} + {\mu_{pl}P_{p,{ave}}} + {\mu_{fl}P_{f,{ave}}}},{t \in T_{l}}} \\{{P_{Lt} + {\mu_{pf}P_{p,{ave}}} - {\mu_{fl}P_{f,{ave}}}},{t \in T_{f}}} \\{{P_{Lt} - {\mu_{pl}P_{p,{ave}}} - {\mu_{pf}P_{f,{ave}}}},{t \in T_{p}}}\end{matrix} \right.};$

under conventional peak shaving, the generation cost of the thermalpower unit includes a coal consumption cost, and under deep peakshaving, the generation cost of the thermal power unit includes a coalconsumption cost, a life loss cost and an oil input cost,

$C_{G} = \left\{ {\begin{matrix}{{C_{T}\left( P_{t} \right)}\ ,} & {P_{\min} \leq P_{t}\  \leq P_{\max}} \\{{{C_{T}\left( P_{t} \right)} + {C_{life}\left( P_{t} \right)}},} & {P_{n} \leq P_{t}\  < P_{\min}} \\{{{C_{T}\left( P_{t} \right)} + {C_{life}\left( P_{t} \right)} + {C_{u}\left( P_{t} \right)}},} & {P_{m} \leq P_{t}\  < P_{n}}\end{matrix},} \right.$

where P_(max) and P_(min) are respectively an maximum output and aminimum output of the thermal power unit; P_(n) is a steady combustionload of the unit under non-oil-input deep peak shaving; P_(m) is asteady combustion limit load of the unit under oil-input deep peakingshaving; P_(t) is output power of the thermal power unit in the t-thperiod;

the coal consumption cost is

${{C_{T}\left( P_{t} \right)} = {\sum\limits_{t = 1}^{24}\left( {{aP}_{t}^{2} + {bP}_{t}\  + c} \right)}},$

a, b and c being coal consumption cost coefficients of the thermal powerunit;

the life loss cost of the unit under variable-load peak shaving iscalculated by

${C_{life}\left( P_{t} \right)} = \frac{\eta C_{B}}{2{N_{f}\left( P_{t} \right)}}$

by referring to a Manson-Coffin equation, where η is an operationinfluence coefficient of the thermal power unit, indicating the degreeof influence of different operating states on unit loss; N_(f)(P_(t)) isa number of rotor cracking cycles determined by a low-cycle fatiguecurve of a rotor; C_(B) is a cost of buying the thermal power unit;

the oil input cost of the unit is expressed asC_(u)(P_(t))=C_(oil)θ_(oil), where C_(oil) is oil consumption of theunit during steady combustion, and θ_(oil) is an oil price for theseason.

Further, in S6, the constraints after the load transfer, namely thesystem power balance constraint, positive and negative spinning reserveconstraint, thermal power unit output constraint, thermal power unitramp constraint, wind power grid-connection ratio constraint, PV powergrid-connection ratio constraint, peak-low load transfer ratio μ_(pl)constraint, peak-flat load transfer ratio μ_(pf) constraint and flat-lowload transfer ratio μ_(fl) constraint are respectively:

  P_(t) + P_(wt) + P_(pvt) = P_(Dt)  (system  power  balance  constraint)  P_(max) − P_(t) ≥ η₁P_(Dt) + η₂P_(wt)  (positive  spinning  reserve  constraint)  P_(t) − P_(min) ≥ η₃P_(Dt) + η₄P_(wt)  (negative  spinning  reserve  constraint)  P_(min) ≤ P_(t) ≤ P_(max)  (thermal  power  unit  output  constraint)  η_(down) ≤ P_(t) − P_(t − 1) ≤ η_(up)  (thermal  power  unit  ramp  constraint)  0 ≤ μ_(w) ≤ 1  (wind  power  grid-connection  ratio  constraint)  0 ≤ μ_(pv) ≤ 1  (PV  power  grid-connection  ratio  constraint)$\mspace{20mu} {0 \leq \mu_{pl} \leq {\frac{P_{p,{ave}} - P_{l,{ave}}}{P_{p,{ave}}}\mspace{14mu} \left( {{peak}\text{-}{low}\mspace{14mu} {load}\mspace{14mu} {transfer}\mspace{14mu} {ratio}\mspace{14mu} {constraint}} \right)}}$${0 \leq \mu_{pf} \leq {\frac{P_{p,{ave}} - P_{f,{ave}}}{P_{p,{ave}}}\mspace{11mu} \left( {{peak}\text{-}{flat}\mspace{14mu} {load}\mspace{14mu} {transfer}\mspace{14mu} {ratio}\mspace{14mu} {constraint}} \right)}}\mspace{14mu}$$\mspace{20mu} {0 \leq \mu_{fl} \leq {\frac{P_{f,{ave}} - P_{l,{ave}}}{P_{f,{ave}}}\mspace{14mu} \left( {{flat}\text{-}{low}\mspace{14mu} {load}\mspace{14mu} {transfer}\mspace{14mu} {ratio}\mspace{14mu} {constraint}} \right)}}$

where, η₁ and η₃ represent up and down-spinning reserve coefficients ofthe system to the load, respectively; η₂ and η₄ represent up anddown-spinning reserve coefficients of the system to the output power ofthe wind turbine, respectively; η_(down) and η_(up) represent a minimumramp-down rate and a maximum ramp-up rate of the thermal power unit,respectively; P_(Dt) is the load of the system in the t-th period afterthe load transfer; P_(t-1) is the output power of the thermal power unitin a (t−1)-th period.

Further, S7 includes:

S71: generating an initial population as an initial antibody population,and evaluating individuals with the system operating cost as an antibodyaffinity operator;

wherein, the initial population is generated by randomly taking valuesof the variables μ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl) within therespective value ranges as initial values;

the initial antibody population is a vector composed of μ_(w), μ_(pv),μ_(pl), μ_(pf) and μ_(fl);

S72: performing immune operations on the population, including immuneselection, cloning, mutation and cloning suppression:

immune selection: selecting high-quality antibodies according tocalculation results of antibody affinities and concentration in thepopulation, and activating the high-quality antibodies, wherein theantibody affinities are values of the system operating cost; theconcentration is the similarity of each value of the system operatingcost; the high-quality antibodies refer to the corresponding μ_(w),μ_(pv), μ_(pl), μ_(pf) and μ_(fl) in the current population in case ofthe minimum system operating cost;

cloning: cloning the activated antibodies to obtain several copies;

mutation: performing mutation operations on the cloned copies to mutatethe affinities thereof;

cloning suppression: reselecting mutation results, suppressingantibodies with a low affinity, and retaining mutation results ofantibodies with a high affinity, wherein the antibodies with a lowaffinity refer to μ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl) correspondingto a larger value of the system operating cost; and

S73: refreshing the population and replacing an antibody with a loweraffinity in the population with a randomly generated new antibody toform a new population; determining whether a termination condition ismet, that is, whether a current number of iterations is greater than orequal to a maximum number of iterations; if yes, converging to obtainμ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl) corresponding to the minimumsystem operating cost; otherwise, returning to S72.

By utilizing the resources on the demand side, that is, taking measuresto compensate power consumers, the present invention prompts powerconsumers to transfer loads. In addition, the present invention greatlyreduces the peak-to-low load difference, increases the renewable energyconsumption and the flexibility of power system dispatching, andachieves a “win-win” for grid companies and power consumers.

BRIEF DESCRIPTION OF THE DRAWINGS

To describe the technical solutions in the embodiments of the presentinvention or in the prior art more clearly, the accompanying drawingsrequired for describing the embodiments or the prior art are brieflydescribed below. Apparently, the accompanying drawings in the followingdescription merely show some embodiments of the present invention, and aperson of ordinary skill in the art may still derive other drawings fromthese accompanying drawings without creative efforts.

FIG. 1 is a flowchart of a method according to the present invention.

FIG. 2 is a peak shaving process of a thermal power unit according tothe present invention.

FIG. 3 is a daily load curve.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The technical solutions in the embodiments of the present invention areclearly and completely described below with reference to theaccompanying drawings in the embodiments of the present invention.Apparently, the described embodiments are merely a part rather than allof the embodiments of the present invention. All other embodimentsobtained by a person of ordinary skill in the art based on theembodiments of the present invention without creative efforts shouldfall within the protection scope of the present invention.

FIG. 1 shows a flowchart of the present invention. A method fordynamically and economically dispatching a power system based on anoptimal load transfer ratio and an optimal grid connection ratio of windpower and photovoltaic (PV) power includes: first acquiring load data;drawing a load curve; defining a peak load period, a flat load periodand a low load period, and calculating an average load of the peak loadperiod, an average load of the flat load period and an average load ofthe low load period; determining a value range of a peak-low loadtransfer ratio, a value range of a peak-flat load transfer ratio and avalue range of a flat-low load transfer ratio; establishing an objectivefunction that comprehensively considers a generation cost of a thermalpower unit, a wind power purchase cost, a PV power purchase cost and acompensation cost for consumer load transfer; introducing an immunealgorithm (IA) to calculate a grid connection ratio of wind power, agrid connection ratio of PV power, the peak-low load transfer ratio, thepeak-flat load transfer ratio and the flat-low load ratio correspondingto a minimum system operating cost. Specifically, this method includesthe following steps:

S1: by taking a day with 24 periods (each period being 1 h) as adispatching period, acquiring initial data, wherein the initial dataincludes output power of a wind turbine in the system, output power of aPV power station in the system and load power of the system.

Output power P_(w) of the wind turbine in the system, output powerP_(pv) of the PV power station in the system and load power P_(L) of thesystem are expressed as follows:

P _(w) ={P _(w1) ,P _(w2) ,Λ,P _(wr) ,Λ,P _(w24)}

P _(pv) ={P _(pv1) ,P _(pv2) ,Λ,P _(pvt) ,Λ,P _(pv24)}

P _(L) {P _(L1) ,P _(L2) ,Λ,P _(Lt) ,Λ,P _(L24)}

P_(wt) represents output power of the wind turbine in a t-th period,P_(pvt) represents output power of the PV power station in the t-thperiod, and P_(Lt) represents load power in the t-th period, t=1, 2, . .. , 24.

S2: drawing a wind power output curve, a PV power output curve and aload curve according to the acquired initial data.

S3: defining a peak period T_(p), a flat period T_(f) and a low periodT_(l) according to the drawn load curve of the system, and calculatingan average load P_(p,ave) of the peak period, an average load P_(f,ave)of the flat period, an average load P_(l,ave) of the low period before aload transfer.

The peak period T_(p), the flat period T_(f) and the low period T_(l) inS3 are calculated as follows:

S31: calculating a total daily load Q_(L),

$Q_{L} = {\sum\limits_{t = 1}^{24}{P_{Lt}.}}$

S32: calculating an hourly average load Q_(L)/24.

S33: regarding the t-th period as the peak period T_(p) whenP_(Lt)>(1+k)·(Q_(L)/24), regarding the t-th period as the low periodT_(l) when P_(Lt)<(1−k)·(Q_(L)/24) and regarding the t-th period as theflat period T_(f) when (1−k)·(Q_(L)/24)≤P_(Lt)≤(1+k)·(Q_(L)/24), whereink is a dividing factor of a peak-low period, a flat-low period and a lowperiod, 0<k<1.

S4: determining a value range of a peak-low load transfer ratio μ_(pl),a value range of a peak-flat load transfer ratio μ_(pf) and a valuerange of a flat-low load transfer ratio μ_(fl) respectively according tothe P_(p,ave), P_(f,ave) and P_(l,ave) in S3, as follows:

$\left\{ \begin{matrix}{\mu_{pl} \in \left\lbrack {0,\frac{P_{p,{ave}} - P_{l,{ave}}}{P_{p,{ave}}}} \right\rbrack} \\{\mu_{pf} \in \left\lbrack {0,\frac{P_{p,{ave}} - P_{f,{ave}}}{P_{p,{ave}}}} \right\rbrack} \\{\mu_{fl} \in \left\lbrack {0,\frac{P_{f,{ave}} - P_{l,{ave}}}{P_{f,{ave}}}} \right\rbrack}\end{matrix} \right..$

S5: assuming that total load power before and after the load transfer isunchanged, then calculating a system load after the load transfer, andestablishing an objective function minC about a system operating costafter the load transfer, the system operating cost including: ageneration cost of a thermal power unit, a wind power purchase cost, aPV power purchase cost and a compensation cost for consumer loadtransfer.

The objective function is expressed as:

min C=C _(G) +C _(W) +C _(PV) +C _(M)

C_(G), C_(W), C_(PV) and C_(M) represent the generation cost of thethermal power unit, the wind power purchase cost, the PV power purchasecost and the compensation cost for consumer load transfer, respectively.

C_(W)=θ_(w)×Q_(w), where θ_(w) and Q_(w) represent an on-grid wind powertariff and on-grid wind power, respectively.

${Q_{w} = {\mu_{w} \times {\sum\limits_{t = 1}^{24}P_{wt}}}},$

where μ_(w) represents a grid connection ratio of wind power.

C_(PV)=θ_(pv)×Q_(pv), where θ_(pv) and Q_(pv) represent an on-grid PVpower tariff and on-grid PV power, respectively.

${Q_{pv} = {\mu_{pv} \times {\sum\limits_{t = 1}^{24}P_{pvt}}}},$

where μ_(pv) represents a grid connection ratio of PV power.

$C_{M} = \left\{ {\begin{matrix}{{\theta_{m1} \times Q_{tran}},} & {Q_{tran} \leq {\lambda Q_{L}}} \\{{{\theta_{m1} \times \lambda Q_{L}} + {\theta_{m2} \times \left( {Q_{tran} - {\lambda Q_{L}}} \right)}},} & {Q_{tran} > {\lambda Q_{L}}}\end{matrix},} \right.$

where λ is a dividing factor of a conventional load and a deep load,0<λ<1; Q_(tran) is a transferred load,

${Q_{tran} = {\sum\limits_{t = 1}^{24}\left( {P_{Dt} - P_{Lt}} \right)}};$

θ_(m1) is a compensation cost for a conventional load transfer; θ_(m2)is a compensation cost for a deep load transfer. The load of the systemin the t-th period after the load transfer is expressed as:

${P_{Dt} = \left\{ \begin{matrix}{{P_{Lt} + {\mu_{pl}P_{p,{ave}}} + {\mu_{fl}P_{f,{ave}}}},{t \in T_{l}}} \\{{P_{Lt} + {\mu_{pf}P_{p,{ave}}} - {\mu_{fl}P_{f,{ave}}}},{t \in T_{f}}} \\{{P_{Lt} - {\mu_{pl}P_{p,{ave}}} - {\mu_{pf}P_{f,{ave}}}},{t \in T_{p}}}\end{matrix} \right.}.$

Under conventional peak shaving, the generation cost of the thermalpower unit includes a coal consumption cost, and under deep peakshaving, the generation cost of the thermal power unit includes a coalconsumption cost, a life loss cost and an oil input cost.

$C_{G} = \left\{ {\begin{matrix}{{C_{T}\left( P_{t} \right)}\ ,} & {P_{\min} \leq P_{t}\  \leq P_{\max}} \\{{{C_{T}\left( P_{t} \right)} + {C_{life}\left( P_{t} \right)}},} & {P_{n} \leq P_{t}\  < P_{\min}} \\{{{C_{T}\left( P_{t} \right)} + {C_{life}\left( P_{t} \right)} + {C_{u}\left( P_{t} \right)}},} & {P_{m} \leq P_{t}\  < P_{n}}\end{matrix},} \right.$

where P_(max) and P_(min) are respectively an maximum output and aminimum output of the thermal power unit; P_(n) is a steady combustionload of the unit under non-oil-input deep peak shaving; P_(m) is asteady combustion limit load of the unit under oil-input deep peakingshaving; P_(t) is output power of the thermal power unit in the t-thperiod.

FIG. 2 shows a peak shaving process, where:

(1) The coal consumption cost is

${{C_{T}\left( P_{t} \right)} = {\sum\limits_{t = 1}^{24}\left( {{aP_{t}^{2}} + {bP_{t}} + c} \right)}},$

a, b and c are coal consumption cost coefficients of the thermal powerunit.

(2) The unit loss cost under variable-load peak shaving is calculated by

${C_{life}\left( P_{t} \right)} = \frac{\eta C_{B}}{2{N_{f}\left( P_{t} \right)}}$

by referring to a Manson-Coffin equation, where η is an operationinfluence coefficient of the thermal power unit, indicating the degreeof influence of different operating states on unit loss; N_(f)(P_(t)) isa number of rotor cracking cycles determined by a low-cycle fatiguecurve of a rotor; C_(B) is a cost of buying the thermal power unit.

(3) The oil input cost of the unit is expressed asC_(u)(P_(t))=C_(oil)θ_(oil), where C_(oil) is oil consumption of theunit during steady combustion, and θ_(oil) is an oil price for theseason.

S6: entering constraints, including: system power balance constraint,positive and negative spinning reserve constraint, thermal power unitoutput constraint, thermal power unit ramp constraint, wind powergrid-connection ratio μ_(w) constraint, PV power grid-connection ratioμ_(pv) constraint, peak-low load transfer ratio μ_(pl) constraint,peak-flat load transfer ratio μ_(pf) constraint and flat-low loadtransfer ratio μ_(fl) 1 constraint.

In S6, the system power balance constraint, the positive and negativespinning reserve constraint, the thermal power unit output constraint,the thermal power unit ramp constraint, the wind power grid-connectionratio constraint and the PV power grid-connection ratio constraint afterthe load transfer are respectively expressed as:

P _(t) +P _(wt) +P _(pvt) =P _(Dt)

P _(max) −P _(t)≥η₁ P _(Dt)+η₂ P _(wt)

P _(t) −P _(min)≥η₃ P _(Dt)+η₄ P _(wt)

P _(min) ≤P _(t) ≤P _(max)

η_(down) ≤P _(t) −P _(t-1)≤η_(up)

0≤μ_(w)≤1

0≤μ_(pv)≤1

η₁ and η₃ represent up and down-spinning reserve coefficients of thesystem to the load, respectively; η₂ and η₄ represent up anddown-spinning reserve coefficients of the system to the output power ofthe wind turbine, respectively; η_(down) and η_(up) represent a minimumramp-down rate and a maximum ramp-up rate of the thermal power unit,respectively; P_(Dt) is the load of the system in the t-th period afterthe load transfer; P_(t-1) is the output power of the thermal power unitin a (t−1)-th period.

S7: introducing an IA to solve and generate an initial population;performing immune operations on the population, including selection,cloning, mutation and cloning suppression; refreshing the populationuntil a termination condition is met, and obtaining μ_(w), μ_(pv),μ_(pl), μ_(pf) and μ_(fl) corresponding to a minimum system operatingcost.

Specifically:

S71: randomly taking values of the variables μ_(w), μ_(pv), μ_(pl),μ_(pf) and μ_(fl) as initial values within the respective value rangesto generate an initial population as an initial antibody population (avector composed of μ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl)), andevaluating the individuals with the system operating cost as an antibodyaffinity operator.

S72: performing immune operations on the population, including immuneselection, cloning, mutation and cloning suppression:

Immune selection: selecting high-quality antibodies according tocalculation results of antibody affinities and concentration in thepopulation, and activating the high-quality antibodies. The antibodyaffinities are values of the system operating cost. The concentration isthe similarity of each value of the system operating cost. If theconcentration is too high, the operating costs of the current populationare similar, and the search process will be concentrated in a smallarea, which is not conducive to the global search for optimalantibodies. The high-quality antibodies refer to the correspondingμ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl) in the current population incase of the minimum system operating cost.

Cloning: cloning the activated antibodies (μ_(w), μ_(pv), μ_(pl), μ_(pf)and μ_(fl) corresponding to the minimum system operating cost) to obtainseveral copies.

Mutation: performing mutation operations on the cloned copies to mutatethe affinities thereof.

Cloning suppression: reselecting mutation results, suppressingantibodies with a low affinity, and retaining mutation results ofantibodies with a high affinity. The low affinity refers to a largervalue instead of the minimum value of the system operating cost. Theantibodies with a low affinity refer to μ_(w), μ_(pv), μ_(pl), μ_(pf)and μ_(fl) corresponding to a larger value of the system operating cost.The high affinity refers to the minimum value of the system operatingcost.

S73: refreshing the population and replacing an antibody with a loweraffinity in the population with a randomly generated new antibody toform a new population; determining whether a termination condition ismet, that is, whether a current number of iterations is greater than orequal to a maximum number of iterations; if yes, converging to obtainμ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl) corresponding to the minimumsystem operating cost; otherwise, returning to S72.

The present invention first gives the definitions and calculationmethods of the wind power grid-connection ratio, the PV powergrid-connection ratio, the peak-low load transfer ratio, the peak-flatload transfer ratio and the flat-low load transfer ratio. The inventionthen determines the system load after the load transfer based on theprinciple that the total load power is unchanged before and after theload transfer, and establishes an objective function and constraintsafter the load transfer. Finally, the present invention introduces an IAto calculate the optimal values of the wind power grid-connection ratio,the PV power grid-connection ratio, the peak-low load transfer ratio,the peak-flat load transfer ratio and the flat-low load transfer ratioand the minimum system operating cost.

In the present invention, when the wind power and PV power in the powersystem are 100% connected to the grid, the economic benefits of thepower system operation are not necessarily maximized. Especially duringthe low load period, the full consumption of the renewable wind powerand PV power requires the shutdown of a large number of thermal powerunits, which greatly increases the cost of peak shaving. The curtailmentof a certain percentage of wind power and PV power can reduce the costof peak shaving and frequency control of the thermal power units andincrease the flexibility of power system dispatching. A load transfer,for example, a transfer of part of the peak load to the low load, cangreatly reduce the peak-low load difference and the start-stop cost ofthe thermal power units. Since the load transfer needs to give consumersa corresponding compensation cost, the system operating cost must have aminimum. At this time, the wind power grid-connection ratio, the PVpower grid-connection ratio, the peak-low load transfer ratio, thepeak-flat load transfer ratio and the flat-low load transfer ratio areoptimal.

FIG. 3 is a curve drawn by load data (before a load transfer) within adispatching period (24 h a day), showing the load (before the loadtransfer) of each time period.

Table 1 shows the wind power output data in a dispatching period, Table2 shows the PV power output data in a dispatching period, Table 3 showsthe parameters of a thermal power unit, and Table 4 shows a comparisonof new energy consumption and system operating cost. Under thetraditional optimized dispatching method, the renewable energyconsumption is 3843.7 MW·h, and the system operating cost is 386,400yuan. Under the optimized dispatching method that only considers theoptimal grid connection ratio of wind power and PV power or the optimalload transfer ratio, the renewable energy consumption increases by 274.6MW·h and 549.1 MW·h, respectively, and the system operating costdecreases by 43,600 yuan and 62,700 yuan, respectively. Under theoptimized dispatching method that considers both the optimal gridconnection ratio of wind power and PV power and the optimal loadtransfer ratio, the renewable energy consumption reaches 5216.5 MW·h,and the minimum system operating cost is 281,500 yuan. The optimizeddispatching method that considers both the optimal grid connection ratioof wind power and PV power and the optimal load transfer ratio greatlyincreases the new energy consumption and decreases the system operatingcost.

TABLE 1 Wind power output forecast Time Wind Power Period Output/MW 1252 2 247.5 3 244 4 243 5 240 6 225 7 210 8 180 9 135 10 120 11 108 12111 13 129 14 114 15 129 16 150 17 180 18 210 19 225 20 222 21 231 22240 23 255 24 270

TABLE 2 PV power output forecast Time PV Power Period Output/MW 7 5.5 825 9 65 10 100 11 115 12 125 13 115 14 110 15 80 16 50 17 25 18 5

TABLE 3 Parameters of thermal power unit Parameter a b c P_(max) P_(min)η_(up) η_(down) Value 0.00285 9.8 131 380 120 4.20 −4.20

TABLE 4 Comparison of new energy consumption and system operating costRenewable Energy System Consumption/ Operating Dispatching Method MW · hCost/yuan Traditional optimized dispatching 3843.7 386,400 Optimaldispatching only considering 4118.3 342,800 optimal grid connectionratio of wind power and PV power Optimal dispatching only considering4392.8 323,700 optimal load transfer ratio Optimal dispatchingconsidering both 5216.5 281,500 optimal grid connection ratio of windpower and PV power and optimal load transfer ratio

Each embodiment of the present specification is described in a relatedmanner, each embodiment focuses on the difference from otherembodiments, and the same and similar parts between the embodiments mayrefer to each other. For a system embodiment, since it corresponds tothe method embodiment, the description is relatively simple, andreference can be made to the description of the method embodiment.

The above described are merely preferred embodiments of the presentinvention, and are not intended to limit the protection scope of thepresent invention. Any modifications, equivalent substitutions andimprovements made within the spirit and scope of the present inventionshould fall within the protection scope of the present invention.

What is claimed is:
 1. A method for dynamically and economicallydispatching a power system based on an optimal load transfer ratio andan optimal grid connection ratio of wind power and photovoltaic (PV)power, comprising the following steps: S1: acquiring initial data bytaking a day with 24 periods (each period being 1 h) as a dispatchingperiod, wherein the initial data comprises an output power P_(w) of awind turbine in the power system, an output power P_(pv) of a PV powerstation in the power system and a load power P_(L) of the power system;S2: drawing a wind power output curve, a PV power output curve and aload curve according to the initial data; S3: defining a peak periodT_(p), a flat period T_(f) and a low period T_(l) according to the loadcurve of the power system, and calculating an average load P_(p,ave) ofthe peak period, an average load P_(f,ave) of the flat period, anaverage load P_(l,ave) of the low period before a load transfer; S4:determining a value range of a peak-low load transfer ratio μ_(pl), avalue range of a peak-flat load transfer ratio μ_(pf) and a value rangeof a flat-low load transfer ratio μ_(fl) respectively according to theP_(p,ave), P_(f,ave) and P_(l,ave) in S3; S5: assuming that a total loadpower before and after the load transfer is unchanged, then calculatinga system load after the load transfer, and establishing an objectivefunction minC about a system operating cost after the load transfer,wherein the system operating cost comprises: a generation cost C_(G) ofa thermal power unit, a wind power purchase cost C_(W), a PV powerpurchase cost C_(PV) and a compensation cost C_(M) for a consumer loadtransfer; S6: entering constraints, comprising: a system power balanceconstraint, a positive spinning reserve constraint, a negative spinningreserve constraint, a thermal power unit output constraint, a thermalpower unit ramp constraint, a wind power grid-connection ratioconstraint, a PV power grid-connection ratio constraint, a peak-low loadtransfer ratio constraint, a peak-flat load transfer ratio constraintand a flat-low load transfer ratio constraint; S7: introducing an immunealgorithm (IA) to solve and generate an initial population; performingimmune operations on the initial population, wherein the immuneoperations comprise an immune selection, a cloning, a mutation and acloning suppression; refreshing the initial population until atermination condition is met, and obtaining μ_(w), μ_(pv), μ_(pl),μ_(pf) and μ_(fl) corresponding to a minimum system operating cost. 2.The method for dynamically and economically dispatching the power systembased on the optimal load transfer ratio and the optimal grid connectionratio of wind power and PV power according to claim 1, wherein in S1,the output power P_(w) of the wind turbine in the power system, theoutput power P_(pv) of the PV power station in the power system and theload power P_(L) of the power system are expressed as follows:P _(w) ={P _(w1) ,P _(w2) ,Λ,P _(wr) ,Λ,P _(w24)}P _(pv) ={P _(pv1) ,P _(pv2) ,Λ,P _(pvt) ,Λ,P _(pv24)}P _(L) {P _(L1) ,P _(L2) ,Λ,P _(Lt) ,Λ,P _(L24)} wherein, P_(wt)represents an output power of the wind turbine in a t-th period, P_(pvt)represents an output power of the PV power station in the t-th period,and P_(Lt) represents a load power in the t-th period, t=1, 2, . . . ,24.
 3. The method for dynamically and economically dispatching the powersystem based on the optimal load transfer ratio and the optimal gridconnection ratio of wind power and PV power according to claim 2,wherein the peak period T_(p), the flat period T_(f) and the low periodT_(l) in S3 are calculated as follows: S31: calculating a total dailyload Q_(L), wherein ${Q_{L} = {\sum\limits_{t = 1}^{24}P_{Lt}}};$ S32:calculating an hourly average load Q_(L)/24; S33: regarding the t-thperiod as the peak period T_(p) when P_(Lt)>(1+k)·(Q_(L)/24) regardingthe t-th period as the low period T_(l) when P_(Lt)<(1−k)·(Q_(L)/24),and regarding the t-th period as the flat period T_(f) when(1−k)·(Q_(L)/24)≤P_(Lt)≤(1+k)·(Q_(L)/24), wherein k is a dividing factorof a peak-low period, a flat-low period and the low period, 0<k<1. 4.The method for dynamically and economically dispatching the power systembased on the optimal load transfer ratio and the optimal grid connectionratio of wind power and PV power according to claim 3, wherein S4 ofdetermining the value range of the peak-low load transfer ratio μ_(pl),the value range of the peak-flat load transfer ratio μ_(pf) and thevalue range of the flat-low load transfer ratio μ_(fl) respectivelyaccording to the P_(p,ave), P_(f,ave) and P_(l,ave) in S3 is as follows:$\left\{ \begin{matrix}{\mu_{pl} \in \left\lbrack {0,\frac{P_{p,{ave}} - P_{l,{ave}}}{P_{p,{ave}}}} \right\rbrack} \\{\mu_{pf} \in \left\lbrack {0,\frac{P_{p,{ave}} - P_{f,{ave}}}{P_{p,{ave}}}} \right\rbrack} \\{\mu_{fl} \in \left\lbrack {0,\frac{P_{f,{ave}} - P_{l,{ave}}}{P_{f,{ave}}}} \right\rbrack}\end{matrix} \right..$
 5. The method for dynamically and economicallydispatching the power system based on the optimal load transfer ratioand the optimal grid connection ratio of wind power and PV poweraccording to claim 4, wherein in S5, the objective function minC isexpressed as:min C=C _(G) +C _(W) +C _(PV) +C _(M) wherein, C_(W)=θ_(w)×Q_(w), θ_(w)and Q_(w) represent an on-grid wind power tariff and an on-grid windpower, respectively;${Q_{w} = {\mu_{w} \times {\sum\limits_{t = 1}^{24}P_{wt}}}},$ μ_(w)represents a grid connection ratio of wind power; C_(PV)=θ_(pv)×Q_(pv),θ_(pv) and Q_(pv) represent an on-grid PV power tariff and an on-grid PVpower, respectively;${Q_{pv} = {\mu_{pv} \times {\sum\limits_{t = 1}^{24}P_{pvt}}}},$μ_(pv) represents a grid connection ratio of PV power;$C_{M} = \left\{ {\begin{matrix}{{\theta_{m\; 1} \times Q_{tran}},} & {Q_{tran} \leq {\lambda Q_{L}}} \\{{{\theta_{m\; 1} \times \lambda Q_{L}} + {\theta_{m\; 2} \times \left( {Q_{tran} - {\lambda Q_{L}}} \right)}},} & {Q_{tran} > {\lambda Q_{L}}}\end{matrix},} \right.$ λ is a dividing factor of a conventional loadand a deep load, 0<λ<1; Q_(tran) is a transferred load,${Q_{tran} = {\sum\limits_{t = 1}^{24}\left( {P_{Dt} - P_{Lt}} \right)}};$θ_(m1) is a compensation cost for a conventional load transfer; θ_(m2)is a compensation cost for a deep load transfer; a load of the powersystem in the t-th period after the load transfer is expressed as:${P_{Dt} = \left\{ \begin{matrix}{{P_{Lt} + {\mu_{pl}P_{p,{ave}}} + {\mu_{fl}P_{f,{ave}}}},{t \in T_{l}}} \\{{P_{Lt} + {\mu_{pf}P_{p,{ave}}} - {\mu_{fl}P_{f,{ave}}}},{t \in T_{f}}} \\{{P_{Lt} - {\mu_{pl}P_{p,{ave}}} - {\mu_{pf}P_{f,{ave}}}},{t \in T_{p}}}\end{matrix} \right.};$ under a conventional peak shaving, thegeneration cost C_(G) of the thermal power unit comprises a coalconsumption cost, and under a deep peak shaving, the generation costC_(G) of the thermal power unit comprises a coal consumption cost, alife loss cost and an oil input cost, $C_{G} = \left\{ {\begin{matrix}{{C_{T}\left( P_{t} \right)},} & {P_{\min} \leq P_{t} \leq P_{\max}} \\{{{C_{T}\left( P_{t} \right)} + {C_{life}\left( P_{t} \right)}},} & {\ {P_{n} \leq P_{t} < P_{\min}}} \\{{{C_{T}\left( P_{t} \right)} + {C_{life}\left( P_{t} \right)} + {C_{u}\left( P_{t} \right)}},} & {P_{m} \leq P_{t} < P_{n}}\end{matrix},} \right.$ wherein P_(max) and P_(min) are respectively amaximum output and a minimum output of the thermal power unit; P_(n) isa steady combustion load of the thermal power unit under a non-oil-inputdeep peak shaving; P_(m) is a steady combustion limit load of thethermal power unit under an oil-input deep peaking shaving; P_(t) is anoutput power of the thermal power unit in the t-th period; the coalconsumption cost is${{C_{T}\left( P_{t} \right)} = {\sum\limits_{t = 1}^{24}\left( {{aP}_{t}^{2} + {bP}_{t}\  + c} \right)}},$a, b and c are coal consumption cost coefficients of the thermal powerunit; the life loss cost of the thermal power unit under a variable-loadpeak shaving is calculated by${C_{life}\left( P_{t} \right)} = \frac{\eta C_{B}}{2{N_{f}\left( P_{t} \right)}}$by referring to a Manson-Coffin equation, wherein η is an operationinfluence coefficient of the thermal power unit, indicating a degree ofinfluence of different operating states on a unit loss; N_(f)(P_(t)) isa number of rotor cracking cycles determined by a low-cycle fatiguecurve of a rotor; C_(B) is a cost of buying the thermal power unit; theoil input cost of the thermal power unit is expressed asC_(u)(P_(t))=C_(oil)θ_(oil), wherein C_(oil) is an oil consumption ofthe thermal power unit during a steady combustion, and θ_(oil) is an oilprice for the season.
 6. The method for dynamically and economicallydispatching the power system based on the optimal load transfer ratioand the optimal grid connection ratio of wind power and PV poweraccording to claim 5, wherein in S6, the constraints after the loadtransfer comprise: the system power balance constraint:P_(t)+P_(wt)+P_(pvt)=P_(Dt) the positive spinning reserve constraint:P_(max)−P_(t)≥η₁P_(Dt)+η₂P_(wt) the negative spinning reserveconstraint: P_(t)−P_(min)≥η₃P_(Dt)+η₄P_(wt) the thermal power unitoutput constraint: P_(min)≤P_(t)≤P_(max) the thermal power unit rampconstraint: η_(down)≤P_(t)−P_(t-1)≤η_(up) the wind power grid-connectionratio constraint: 0≤μ_(w)≤1 the PV power grid-connection ratioconstraint: 0≤μ_(pv)≤1 the peak-low load transfer ratio constraint:$0 \leq \mu_{pl} \leq \frac{P_{p,{ave}} - P_{l,{ave}}}{P_{p,{ave}}}$ thepeak-flat load transfer ratio constraint:$0 \leq \mu_{pf} \leq \frac{P_{p,{ave}} - P_{f,{ave}}}{P_{p,{ave}}}$ theflat-low load transfer ratio constraint:$0 \leq \mu_{fl} \leq \frac{P_{f,{ave}} - P_{l,{ave}}}{P_{f,{ave}}}$wherein, η₁ and η₃ represent an up-spinning reserve coefficient and adown-spinning reserve coefficient of the power system to the load,respectively; η₂ and η₄ represent an up-spinning reserve coefficient anda down-spinning reserve coefficient of the power system to the outputpower of the wind turbine, respectively; η_(down) and η_(up) represent aminimum ramp-down rate and a maximum ramp-up rate of the thermal powerunit, respectively; P_(Dt) is the load of the power system in the t-thperiod after the load transfer; P_(t-1) is an output power of thethermal power unit in a (t−1)-th period.
 7. The method for dynamicallyand economically dispatching the power system based on the optimal loadtransfer ratio and the optimal grid connection ratio of wind power andPV power according to claim 6, wherein S7 comprises: S71: generating theinitial population as an initial antibody population, and evaluatingindividuals in the initial antibody population with the system operatingcost as an antibody affinity operator; wherein, the initial populationis generated by randomly taking values of the variables μ_(w), μ_(pv),μ_(pl), μ_(pf) and μ_(fl) within the respective value ranges as initialvalues; the initial antibody population is a vector composed of μ_(w),μ_(pv), μ_(pl), μ_(pf) and μ_(fl); S72: performing immune operations onthe initial antibody population, wherein the immune operations comprisethe immune selection, the cloning, the mutation and the cloningsuppression: the immune selection: selecting high-quality antibodiesaccording to calculation results of antibody affinities andconcentration in the initial antibody population, and activating thehigh-quality antibodies to obtain activated antibodies, wherein theantibody affinities are values of the system operating cost; theconcentration is a similarity of each value of the system operatingcost; the high-quality antibodies refer to the corresponding μ_(w),μ_(pv), μ_(pl), μ_(pf) and μ_(fl) in the current population in case ofthe minimum system operating cost; the cloning: cloning the activatedantibodies to obtain a plurality of copies of the activated antibodies;the mutation: performing mutation operations on the plurality of copiesof the activated antibodies to mutate the antibody affinities; thecloning suppression: reselecting mutation results, suppressingantibodies with a low affinity, and retaining mutation results ofantibodies with a high affinity, wherein the antibodies with the lowaffinity refer to μ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl) correspondingto a larger value of the system operating cost; and S73: refreshing theinitial antibody population and replacing the antibodies with the lowaffinity in the initial antibody population with another randomlygenerated antibody to form an another antibody population; determiningwhether the termination condition is met, wherein the terminationcondition is that a current number of iterations is greater than orequal to a maximum number of iterations; if the termination condition ismet, converging to obtain μ_(w), μ_(pv), μ_(pl), μ_(pf) and μ_(fl)corresponding to the minimum system operating cost; if the terminationcondition is not met, returning to S72.